• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.2924-2944
• /
• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• 식물병연구
• /
• 제16권2호
• /
• pp.125-128
• /
• 2010

자두 주산지인 경북 김천을 비롯하여 의성, 경산, 군위지역에 발생하는 자두과실의 검은점무늬병 병원균을 조기 검출하기 위하여 integrons를 이용한 primer를 제작하고 DNA 추출향상을 위해 전배양법을 그리고 검출감도 향상을 위해 nested PCR을 시도하였다. 제작된 integrons primer로 병반 시료를 PCR한 결과 예상되는 크기인 760 bp로 증폭이 되어 검은점무늬병의 원인 병원균은 Xanthomonas arboricola pv. pruni로 확인되었고 진단용 primer로 사용 될 수 있음을 확인할 수 있었다. DNA 추출 방법별로 PCR 진단효율은 조사한 결과 직접 추출하는 방법보다는 10% LB배지로 전배양한 다음 DNA를 추출하는 방법이 더 효과적이었고 nested PCR을 이용할 경우에는 과원내 강우중의 X. arboricola pv. pruni의 검출도 가능하였다. 이번에 개발된 Integrons primer를 이용, 전배양법과 nested PCR을 실시할 경우 1일 이내에 병징이 나타나지 않은 유묘에서의 X. arboricola pv. pruni 감염여부나 식물검역에서의 X. arboricola pv. pruni 감염식물 진단 등에도 활용이 가능할 것으로 생각된다.

• 한국동물위생학회지
• /
• 제37권3호
• /
• pp.213-218
• /
• 2014

In early April 2014, a month-old Alexandrine Paraqeet (Psittacula eupatria) that was raised in a domestic aviary located in Gyungju-si, Korea was suddenly died and submitted to Animal Disease Intervention Center, Kyungpook National University in order to diagnose the causative agent. In post-mortem examination, the bird had abnormally developed feathers on the neck and abdomen region and subcutaneous hemorrhages on the neck and cheek adjacent to the beak. At necropsy, the bird had hemorrhage on the muscle of the femoral region, ascites, multi-focal hemorrhages on the epicardium, and diffuse hemorrhages on the sub-serosa of proventriculus and gizzard, suggesting typical avian polyomavirus (APV) infection. The partial large tumor (T) antigen gene of APV was detected by PCR from tissues of the heart, lung, liver, kidney, proventriculus and feathers of the APV-suspected birds. However, other pathogenic virus-specific nucleic acid common with psittacine birds such as avian bornavirus, psittacine beak and feather disease virus and psittacid herpesvirus were not detected from the mixed tissue samples of the bird, indicating this case is due to single infection of APV. Nucleotide sequence analysis of the partially amplified large T antigen DNA was confirmed to have 99~100% homology with that of the previously reported APV strains. This case report describes the first detection of APV in Alexandrine Paraqeet in Korea.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2022년도 추계학술발표대회
• /
• pp.556-558
• /
• 2022

Diabetic retinopathy (DR) is one of the leading diseases causing vision loss. Early detection of this disease has a crucial role in protecting patients' eyes. Recent works have achieved impressive result when performing DR detection on fundus images using deep learning. In the deep learning-based approach, data augmentation has significant impact on the result. Recently, many data augmentation policies have been proposed and achieved state-of-the-art performance on different tasks. In this work, we compare effects of three data augmentation policies on DR grading in fundus images.

• 대한약학회:학술대회논문집
• /
• 대한약학회 2002년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
• /
• pp.291.3-292
• /
• 2002

Cancer is multifaceted disease that presents many challenges to clinicians and cancer researchers searching for more effective ways to combat its often devastating effects. Among the central challenges of this disease, are the identification of markers for improved diagnosis and classification of tumors, and the definition of targets for more effective therapeutic measures. The objective of this study is to identify potential biomarkers for the early detection of gastric cancer in serum. (omitted)

• Journal of Periodontal and Implant Science
• /
• 제43권3호
• /
• pp.111-120
• /
• 2013

Periodontitis is a common oral disease that is characterized by infection and inflammation of the tooth supporting tissues. While its incidence is highly associated with outgrowth of the pathogenic microbiome, some patients show signs of predisposition and quickly fall into recurrence after treatment. Recent research using genetic associations of candidates as well as genome-wide analysis highlights that variations in genes related to the inflammatory response are associated with an increased risk of periodontitis. Intriguingly, some of the genes are regulated by epigenetic modifications, supposedly established and reprogrammed in response to environmental stimuli. In addition, the treatment with epigenetic drugs improves treatment of periodontitis in a mouse model. In this review, we highlight some of the recent progress identifying genetic factors associated with periodontitis and point to promising approaches in epigenetic research that may contribute to the understanding of molecular mechanisms involving different responses in individuals and the early detection of predispositions that may guide in future oral treatment and disease prevention.

• 생물정신의학
• /
• 제20권3호
• /
• pp.104-110
• /
• 2013

Objectives : The aim of this study was to examine the prospective impact of the apolipoprotein E (APOE) ${\varepsilon}4$ on cognitive performance in the community-dwelling elderly individuals with Alzheimer's disease (AD). Methods : The total number of subjects was 30 (12 men and 18 women) who were diagnosed with AD from a Korean project of "Early Detection of Dementia". People aged 65-85 years were included in the analysis. The eight neuropsychological domains from the Korean version of Consortium to Establish a Registry of Alzheimer's Disease (CERAD-K) were conducted to test subjects. They have been followed at 24-month intervals with the same assessments at each interval. Their cognitive performance at 2 year intervals was compared by the occurrence of the APOE ${\varepsilon}4$. Results : The impact of ${\varepsilon}4$ allele was significant in the Word List Memory Test (WLMT, F = 4.345, df = 1, p = 0.021) and Word List Recall Test (WLRT, F = 5.569, df = 1, p = 0.033). Conclusions : The APOE ${\varepsilon}4$ allele was significantly correlated especially with verbal episodic memory domain in community-dwelling elders diagnosed with AD.

• 산업경영시스템학회지
• /
• 제44권3호
• /
• pp.33-38
• /
• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• Child Health Nursing Research
• /
• 제29권3호
• /
• pp.218-228
• /
• 2023

Purpose: This study aimed to identify clinical characteristics of South Korean pediatric inflammatory bowel disease (IBD) in a children's hospital over the past 5 years, with a specific focus on comparing the features observed between Crohn's disease (CD) and ulcerative colitis (UC). Additionally, it aimed to examine the nursing diagnoses given to patients. Methods: This retrospective study analyzed the medical records of Korean pediatric patients under 18 years of age who were diagnosed with IBD and hospitalized at a children's hospital in Seoul, South Korea, from January 2017 to December 2021. Results: The number of pediatric patients diagnosed with IBD steadily increased. This finding was particularly prominent for CD patients, the majority of whom were male. Pediatric patients with CD had significantly higher rates of abdominal pain and perianal lesions, while pediatric patients with UC had a higher rate of bloody stool. Laboratory findings indicated that CD patients had higher levels of inflammatory markers and lower albumin levels than UC patients. The nursing diagnoses given during hospitalization mostly related to safety and protection, physical comfort, and gastrointestinal function. Conclusion: This study provides insights into Korean pediatric IBD patients, enabling early detection and the development of nursing intervention strategies. From a comprehensive perspective, nursing care should not only address patients' physical needs but also their psychosocial needs.

• 한국멀티미디어학회논문지
• /
• 제20권2호
• /
• pp.205-215
• /
• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.